What kind of experimental design for finding and checking robustness of analytical methods? Original Research Article

Finding and checking robustness of analytical methods are two different problems which must be treated with different tools. Finding robustness is to discover an experimental region where nothing happens, which means that the response of interest is not influenced by changing significantly the levels of the various operating factors. Such a disclosure is not obvious and needs to use surface response designs associated with canonical analysis. As there are several types of surface response designs, the question is what are the advantages and the drawbacks of each of them. In general, the solution is rarely totally satisfactory. An analytical method could be robust for some factors and not for others. At this stage, the concept of total and partial robustness may be introduced. Checking robustness is to verify the recommended settings of the analytical method. This verification enables also to assess the response variations near the operational point for each factor. Plackett and Burman designs are often proposed for checking analytical method robustness. But these designs must be used carefully. The first precaution is to add several measurements at the central point to estimate the method repeatability and to be sure that the response surface has no curvature. If the response surface presents an important curvature, Plackett and Burman designs are not suitable to check robustness and other experimental designs must be used. In this case, star designs, which are “one factor at a time” designs seems strangely be the best way to verify the robustness of an analytical method.